Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations121387
Missing cells2
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.5 MiB
Average record size in memory160.0 B

Variable types

Text2
Categorical2
Numeric16

Alerts

Deviation of star ratings is highly overall correlated with Rating and 3 other fieldsHigh correlation
FOG Index is highly overall correlated with Flesch Reading EaseHigh correlation
Flesch Reading Ease is highly overall correlated with FOG IndexHigh correlation
Rating is highly overall correlated with Deviation of star ratingsHigh correlation
avg_rating is highly overall correlated with Deviation of star ratings and 3 other fieldsHigh correlation
breadth is highly overall correlated with depthHigh correlation
depth is highly overall correlated with breadthHigh correlation
num_of_enrolled is highly overall correlated with avg_rating and 3 other fieldsHigh correlation
num_of_ratings is highly overall correlated with Deviation of star ratings and 4 other fieldsHigh correlation
num_of_reviews is highly overall correlated with Deviation of star ratings and 4 other fieldsHigh correlation
num_of_top_instructor_courses is highly overall correlated with num_of_top_instructor_leanersHigh correlation
num_of_top_instructor_leaners is highly overall correlated with num_of_enrolled and 3 other fieldsHigh correlation
sentiment_score_discrete is highly overall correlated with valenceHigh correlation
valence is highly overall correlated with sentiment_score_discreteHigh correlation
Rating is highly imbalanced (60.8%) Imbalance
helpfulness is highly skewed (γ1 = 42.77455275) Skewed
helpfulness has 116004 (95.6%) zeros Zeros

Reproduction

Analysis started2025-02-06 04:42:00.377315
Analysis finished2025-02-06 04:42:15.254865
Duration14.88 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Distinct205
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size948.5 KiB
2025-02-06T13:42:15.451333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length79
Median length58
Mean length21.21714
Min length2

Characters and Unicode

Total characters2575485
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowfoundations-of-cybersecurity
2nd rowfoundations-of-cybersecurity
3rd rowfoundations-of-cybersecurity
4th rowfoundations-of-cybersecurity
5th rowfoundations-of-cybersecurity
ValueCountFrequency (%)
python-data 9610
 
7.9%
python 9486
 
7.8%
foundations-user-experience-design 8989
 
7.4%
python-network-data 5735
 
4.7%
html 4905
 
4.0%
foundations-of-cybersecurity 4621
 
3.8%
matlab 3338
 
2.7%
introduction-tensorflow 3303
 
2.7%
html-css-javascript-for-web-developers 3235
 
2.7%
python-basics 2932
 
2.4%
Other values (195) 65233
53.7%
2025-02-06T13:42:15.722717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 236320
 
9.2%
e 218338
 
8.5%
t 217923
 
8.5%
o 204584
 
7.9%
n 197417
 
7.7%
a 172715
 
6.7%
r 162801
 
6.3%
i 160660
 
6.2%
s 142672
 
5.5%
d 104618
 
4.1%
Other values (20) 757437
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2575485
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 236320
 
9.2%
e 218338
 
8.5%
t 217923
 
8.5%
o 204584
 
7.9%
n 197417
 
7.7%
a 172715
 
6.7%
r 162801
 
6.3%
i 160660
 
6.2%
s 142672
 
5.5%
d 104618
 
4.1%
Other values (20) 757437
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2575485
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 236320
 
9.2%
e 218338
 
8.5%
t 217923
 
8.5%
o 204584
 
7.9%
n 197417
 
7.7%
a 172715
 
6.7%
r 162801
 
6.3%
i 160660
 
6.2%
s 142672
 
5.5%
d 104618
 
4.1%
Other values (20) 757437
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2575485
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 236320
 
9.2%
e 218338
 
8.5%
t 217923
 
8.5%
o 204584
 
7.9%
n 197417
 
7.7%
a 172715
 
6.7%
r 162801
 
6.3%
i 160660
 
6.2%
s 142672
 
5.5%
d 104618
 
4.1%
Other values (20) 757437
29.4%

Rating
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size948.5 KiB
5
99607 
4
15142 
3
 
4152
2
 
1371
1
 
1115

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121387
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 99607
82.1%
4 15142
 
12.5%
3 4152
 
3.4%
2 1371
 
1.1%
1 1115
 
0.9%

Length

2025-02-06T13:42:15.826832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-06T13:42:15.853236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
5 99607
82.1%
4 15142
 
12.5%
3 4152
 
3.4%
2 1371
 
1.1%
1 1115
 
0.9%

Most occurring characters

ValueCountFrequency (%)
5 99607
82.1%
4 15142
 
12.5%
3 4152
 
3.4%
2 1371
 
1.1%
1 1115
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 121387
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 99607
82.1%
4 15142
 
12.5%
3 4152
 
3.4%
2 1371
 
1.1%
1 1115
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 121387
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 99607
82.1%
4 15142
 
12.5%
3 4152
 
3.4%
2 1371
 
1.1%
1 1115
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 121387
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 99607
82.1%
4 15142
 
12.5%
3 4152
 
3.4%
2 1371
 
1.1%
1 1115
 
0.9%

avg_rating
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7471805
Minimum3.8
Maximum4.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size948.5 KiB
2025-02-06T13:42:15.880303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile4.6
Q14.7
median4.8
Q34.8
95-th percentile4.9
Maximum4.9
Range1.1
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.10425995
Coefficient of variation (CV)0.0219625
Kurtosis4.2441509
Mean4.7471805
Median Absolute Deviation (MAD)0.1
Skewness-1.3557117
Sum576246
Variance0.010870138
MonotonicityNot monotonic
2025-02-06T13:42:15.908250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
4.8 58959
48.6%
4.7 30831
25.4%
4.6 14624
 
12.0%
4.9 12412
 
10.2%
4.5 2829
 
2.3%
4.4 1087
 
0.9%
4.3 498
 
0.4%
4.1 56
 
< 0.1%
4 35
 
< 0.1%
3.9 28
 
< 0.1%
Other values (2) 28
 
< 0.1%
ValueCountFrequency (%)
3.8 8
 
< 0.1%
3.9 28
 
< 0.1%
4 35
 
< 0.1%
4.1 56
 
< 0.1%
4.2 20
 
< 0.1%
4.3 498
 
0.4%
4.4 1087
 
0.9%
4.5 2829
 
2.3%
4.6 14624
12.0%
4.7 30831
25.4%
ValueCountFrequency (%)
4.9 12412
 
10.2%
4.8 58959
48.6%
4.7 30831
25.4%
4.6 14624
 
12.0%
4.5 2829
 
2.3%
4.4 1087
 
0.9%
4.3 498
 
0.4%
4.2 20
 
< 0.1%
4.1 56
 
< 0.1%
4 35
 
< 0.1%

num_of_ratings
Real number (ℝ)

High correlation 

Distinct193
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40131.903
Minimum13
Maximum229752
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size948.5 KiB
2025-02-06T13:42:15.944041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile1247
Q13833
median14713
Q344178
95-th percentile229752
Maximum229752
Range229739
Interquartile range (IQR)40345

Descriptive statistics

Standard deviation61828.438
Coefficient of variation (CV)1.5406306
Kurtosis4.0275906
Mean40131.903
Median Absolute Deviation (MAD)12158
Skewness2.2202018
Sum4.8714913 × 109
Variance3.8227558 × 109
MonotonicityNot monotonic
2025-02-06T13:42:15.985213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96077 9610
 
7.9%
229752 9486
 
7.8%
69498 8989
 
7.4%
44178 5735
 
4.7%
27564 4905
 
4.0%
27905 4621
 
3.8%
17700 3338
 
2.7%
19521 3303
 
2.7%
16672 3235
 
2.7%
17773 2932
 
2.4%
Other values (183) 65233
53.7%
ValueCountFrequency (%)
13 1
 
< 0.1%
19 6
 
< 0.1%
24 23
< 0.1%
28 2
 
< 0.1%
30 5
 
< 0.1%
39 16
< 0.1%
41 8
 
< 0.1%
42 10
< 0.1%
52 11
< 0.1%
55 11
< 0.1%
ValueCountFrequency (%)
229752 9486
7.8%
96077 9610
7.9%
69498 8989
7.4%
44178 5735
4.7%
27905 4621
3.8%
27564 4905
4.0%
21320 2087
 
1.7%
19521 3303
 
2.7%
17773 2932
 
2.4%
17700 3338
 
2.7%

helpfulness
Real number (ℝ)

Skewed  Zeros 

Distinct76
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17410431
Minimum0
Maximum239
Zeros116004
Zeros (%)95.6%
Negative0
Negative (%)0.0%
Memory size948.5 KiB
2025-02-06T13:42:16.023981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum239
Range239
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.1063695
Coefficient of variation (CV)12.098319
Kurtosis3074.0323
Mean0.17410431
Median Absolute Deviation (MAD)0
Skewness42.774553
Sum21134
Variance4.4367924
MonotonicityNot monotonic
2025-02-06T13:42:16.065668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 116004
95.6%
1 2984
 
2.5%
2 756
 
0.6%
3 367
 
0.3%
4 268
 
0.2%
5 173
 
0.1%
6 130
 
0.1%
7 90
 
0.1%
8 86
 
0.1%
9 78
 
0.1%
Other values (66) 451
 
0.4%
ValueCountFrequency (%)
0 116004
95.6%
1 2984
 
2.5%
2 756
 
0.6%
3 367
 
0.3%
4 268
 
0.2%
5 173
 
0.1%
6 130
 
0.1%
7 90
 
0.1%
8 86
 
0.1%
9 78
 
0.1%
ValueCountFrequency (%)
239 1
< 0.1%
202 1
< 0.1%
144 1
< 0.1%
138 1
< 0.1%
136 1
< 0.1%
118 1
< 0.1%
114 1
< 0.1%
111 1
< 0.1%
99 1
< 0.1%
87 1
< 0.1%
Distinct121351
Distinct (%)> 99.9%
Missing1
Missing (%)< 0.1%
Memory size948.5 KiB
2025-02-06T13:42:16.270792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5655
Median length1971
Mean length128.24104
Min length1

Characters and Unicode

Total characters15566667
Distinct characters75
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique121323 ?
Unique (%)99.9%

Sample

1st rowThe course is well paced and they get you comfortable with the topics even though we do not have any sort of prior exposure in this field. It is very good for the beginners who are new to this field
2nd rowInformation was well organized, easy to learn, and study. with frequent note writing, and some breaks . You can learn a good brief summary of what's to come, and what to research more in the future.
3rd rowFor a foundation course, this one was easy to understand, it explained all basic concepts in a fluid way and built up the base for the upcoming courses. I'm eager to move on to the other courses now.
4th rowI think this is a great start for anyone who is starting from absolute zero. I think that since I've been toying with the idea of getting into Cybersecurity for 2 years now, it was a great refresher!
5th rowSurprised by the quality of this course. repeating items so you learn by seeing definitions and concepts over and over again while using great analogy to make difficult concept understandable.
ValueCountFrequency (%)
the 123601
 
4.7%
and 90715
 
3.4%
to 90115
 
3.4%
course 88853
 
3.3%
a 63565
 
2.4%
i 62474
 
2.4%
of 49581
 
1.9%
this 47019
 
1.8%
is 44077
 
1.7%
for 41239
 
1.6%
Other values (46043) 1951407
73.6%
2025-02-06T13:42:16.537098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2544882
16.3%
e 1542155
 
9.9%
t 1052378
 
6.8%
o 1002649
 
6.4%
a 915063
 
5.9%
n 876162
 
5.6%
r 829709
 
5.3%
s 820077
 
5.3%
i 799167
 
5.1%
l 534871
 
3.4%
Other values (65) 4649554
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15566667
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2544882
16.3%
e 1542155
 
9.9%
t 1052378
 
6.8%
o 1002649
 
6.4%
a 915063
 
5.9%
n 876162
 
5.6%
r 829709
 
5.3%
s 820077
 
5.3%
i 799167
 
5.1%
l 534871
 
3.4%
Other values (65) 4649554
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15566667
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2544882
16.3%
e 1542155
 
9.9%
t 1052378
 
6.8%
o 1002649
 
6.4%
a 915063
 
5.9%
n 876162
 
5.6%
r 829709
 
5.3%
s 820077
 
5.3%
i 799167
 
5.1%
l 534871
 
3.4%
Other values (65) 4649554
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15566667
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2544882
16.3%
e 1542155
 
9.9%
t 1052378
 
6.8%
o 1002649
 
6.4%
a 915063
 
5.9%
n 876162
 
5.6%
r 829709
 
5.3%
s 820077
 
5.3%
i 799167
 
5.1%
l 534871
 
3.4%
Other values (65) 4649554
29.9%

num_of_reviews
Real number (ℝ)

High correlation 

Distinct178
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4175.094
Minimum1
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size948.5 KiB
2025-02-06T13:42:16.590367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile216
Q1860
median2718
Q37113
95-th percentile10000
Maximum10000
Range9999
Interquartile range (IQR)6253

Descriptive statistics

Standard deviation3713.9884
Coefficient of variation (CV)0.88955803
Kurtosis-1.248118
Mean4175.094
Median Absolute Deviation (MAD)2185
Skewness0.58369948
Sum5.0680213 × 108
Variance13793709
MonotonicityNot monotonic
2025-02-06T13:42:16.632105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 28085
23.1%
7113 5735
 
4.7%
6623 4905
 
4.0%
5510 4621
 
3.8%
4032 3338
 
2.7%
3809 3303
 
2.7%
4131 3235
 
2.7%
3784 2932
 
2.4%
2854 2220
 
1.8%
2718 2159
 
1.8%
Other values (168) 60854
50.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 1
 
< 0.1%
5 5
 
< 0.1%
6 6
 
< 0.1%
7 4
 
< 0.1%
8 31
< 0.1%
9 21
< 0.1%
10 23
< 0.1%
11 22
< 0.1%
12 7
 
< 0.1%
ValueCountFrequency (%)
10000 28085
23.1%
7113 5735
 
4.7%
6623 4905
 
4.0%
5510 4621
 
3.8%
4131 3235
 
2.7%
4032 3338
 
2.7%
3809 3303
 
2.7%
3784 2932
 
2.4%
2854 2220
 
1.8%
2779 1850
 
1.5%

num_of_enrolled
Real number (ℝ)

High correlation 

Distinct205
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean727639.14
Minimum1507
Maximum3205753
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size948.5 KiB
2025-02-06T13:42:16.671573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1507
5-th percentile67311
Q1198585
median459997
Q31076350
95-th percentile3205753
Maximum3205753
Range3204246
Interquartile range (IQR)877765

Descriptive statistics

Standard deviation820419.7
Coefficient of variation (CV)1.127509
Kurtosis3.6395284
Mean727639.14
Median Absolute Deviation (MAD)299287
Skewness2.0538046
Sum8.8325933 × 1010
Variance6.7308848 × 1011
MonotonicityNot monotonic
2025-02-06T13:42:16.714040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1076350 9610
 
7.9%
3205753 9486
 
7.8%
1361423 8989
 
7.4%
682235 5735
 
4.7%
574559 4905
 
4.0%
988223 4621
 
3.8%
497579 3338
 
2.7%
385916 3303
 
2.7%
1171384 3235
 
2.7%
459997 2932
 
2.4%
Other values (195) 65233
53.7%
ValueCountFrequency (%)
1507 2
 
< 0.1%
2165 11
< 0.1%
2233 6
< 0.1%
2240 10
< 0.1%
2429 4
 
< 0.1%
3736 8
< 0.1%
3802 1
 
< 0.1%
4033 9
< 0.1%
5198 7
< 0.1%
5932 8
< 0.1%
ValueCountFrequency (%)
3205753 9486
7.8%
1361423 8989
7.4%
1171384 3235
 
2.7%
1076350 9610
7.9%
988223 4621
3.8%
682235 5735
4.7%
582205 2159
 
1.8%
574559 4905
4.0%
540079 2075
 
1.7%
535924 1936
 
1.6%

num_of_top_instructor_courses
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.57201
Minimum2
Maximum1675
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size948.5 KiB
2025-02-06T13:42:16.802454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q19
median60
Q3129
95-th percentile325
Maximum1675
Range1673
Interquartile range (IQR)120

Descriptive statistics

Standard deviation207.33114
Coefficient of variation (CV)1.8750779
Kurtosis35.751552
Mean110.57201
Median Absolute Deviation (MAD)51
Skewness5.255979
Sum13422005
Variance42986.202
MonotonicityNot monotonic
2025-02-06T13:42:16.836491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
60 34705
28.6%
325 22785
18.8%
9 8841
 
7.3%
4 7591
 
6.3%
129 6315
 
5.2%
5 5313
 
4.4%
18 5172
 
4.3%
2 4368
 
3.6%
12 3812
 
3.1%
8 3753
 
3.1%
Other values (19) 18732
15.4%
ValueCountFrequency (%)
2 4368
3.6%
3 713
 
0.6%
4 7591
6.3%
5 5313
4.4%
6 1336
 
1.1%
7 2268
 
1.9%
8 3753
3.1%
9 8841
7.3%
10 48
 
< 0.1%
12 3812
3.1%
ValueCountFrequency (%)
1675 1442
 
1.2%
325 22785
18.8%
196 65
 
0.1%
129 6315
 
5.2%
87 137
 
0.1%
66 16
 
< 0.1%
60 34705
28.6%
58 1741
 
1.4%
53 1142
 
0.9%
48 9
 
< 0.1%

num_of_top_instructor_leaners
Real number (ℝ)

High correlation 

Distinct83
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3710246.1
Minimum3945
Maximum11153139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size948.5 KiB
2025-02-06T13:42:16.874446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3945
5-th percentile167950
Q1517137
median1245385
Q34384601
95-th percentile11153139
Maximum11153139
Range11149194
Interquartile range (IQR)3867464

Descriptive statistics

Standard deviation3936264.2
Coefficient of variation (CV)1.0609173
Kurtosis-0.38959833
Mean3710246.1
Median Absolute Deviation (MAD)1175823
Skewness1.0120044
Sum4.5037564 × 1011
Variance1.5494176 × 1013
MonotonicityNot monotonic
2025-02-06T13:42:16.916500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4384601 34705
28.6%
11153139 22785
18.8%
954353 6315
 
5.2%
1071441 4957
 
4.1%
517137 3538
 
2.9%
1245385 3536
 
2.9%
506004 3437
 
2.8%
528545 3374
 
2.8%
596976 3139
 
2.6%
426823 3060
 
2.5%
Other values (73) 32541
26.8%
ValueCountFrequency (%)
3945 8
 
< 0.1%
6855 4
 
< 0.1%
7841 19
< 0.1%
8506 35
< 0.1%
8682 7
 
< 0.1%
8703 8
 
< 0.1%
18193 8
 
< 0.1%
19837 18
< 0.1%
21526 31
< 0.1%
29959 40
< 0.1%
ValueCountFrequency (%)
11153139 22785
18.8%
4384601 34705
28.6%
3081791 1142
 
0.9%
2767486 1442
 
1.2%
1245385 3536
 
2.9%
1071441 4957
 
4.1%
1065753 65
 
0.1%
1014757 1741
 
1.4%
954353 6315
 
5.2%
892947 1
 
< 0.1%

text_length
Real number (ℝ)

Distinct354
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.853683
Minimum1
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size948.5 KiB
2025-02-06T13:42:16.956159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q17
median14
Q327
95-th percentile65
Maximum1000
Range999
Interquartile range (IQR)20

Descriptive statistics

Standard deviation26.602132
Coefficient of variation (CV)1.2172837
Kurtosis70.908089
Mean21.853683
Median Absolute Deviation (MAD)9
Skewness5.501928
Sum2652753
Variance707.67342
MonotonicityNot monotonic
2025-02-06T13:42:16.996573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 6518
 
5.4%
4 5958
 
4.9%
6 5957
 
4.9%
7 5405
 
4.5%
8 5040
 
4.2%
9 4610
 
3.8%
3 4451
 
3.7%
10 4432
 
3.7%
11 4077
 
3.4%
2 3958
 
3.3%
Other values (344) 70981
58.5%
ValueCountFrequency (%)
1 1619
 
1.3%
2 3958
3.3%
3 4451
3.7%
4 5958
4.9%
5 6518
5.4%
6 5957
4.9%
7 5405
4.5%
8 5040
4.2%
9 4610
3.8%
10 4432
3.7%
ValueCountFrequency (%)
1000 1
< 0.1%
786 1
< 0.1%
748 1
< 0.1%
728 1
< 0.1%
627 1
< 0.1%
582 1
< 0.1%
544 1
< 0.1%
534 1
< 0.1%
520 1
< 0.1%
512 1
< 0.1%

time_lapsed
Real number (ℝ)

Distinct3379
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1389.5706
Minimum0
Maximum3397
Zeros18
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size948.5 KiB
2025-02-06T13:42:17.035028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile214
Q1776
median1484
Q31700
95-th percentile2889
Maximum3397
Range3397
Interquartile range (IQR)924

Descriptive statistics

Standard deviation770.59645
Coefficient of variation (CV)0.55455724
Kurtosis-0.28076776
Mean1389.5706
Median Absolute Deviation (MAD)511
Skewness0.37767789
Sum1.6867581 × 108
Variance593818.88
MonotonicityNot monotonic
2025-02-06T13:42:17.077101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1644 237
 
0.2%
1663 235
 
0.2%
1645 234
 
0.2%
1657 229
 
0.2%
1659 227
 
0.2%
1601 226
 
0.2%
1643 225
 
0.2%
1664 224
 
0.2%
1650 224
 
0.2%
1656 224
 
0.2%
Other values (3369) 119102
98.1%
ValueCountFrequency (%)
0 18
< 0.1%
1 18
< 0.1%
2 22
< 0.1%
3 17
< 0.1%
4 23
< 0.1%
5 24
< 0.1%
6 24
< 0.1%
7 26
< 0.1%
8 27
< 0.1%
9 29
< 0.1%
ValueCountFrequency (%)
3397 1
< 0.1%
3395 2
< 0.1%
3392 1
< 0.1%
3391 1
< 0.1%
3390 1
< 0.1%
3384 1
< 0.1%
3383 1
< 0.1%
3382 1
< 0.1%
3379 2
< 0.1%
3376 1
< 0.1%

Deviation of star ratings
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.40515706
Minimum0
Maximum3.9
Zeros7
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size948.5 KiB
2025-02-06T13:42:17.116090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.2
median0.2
Q30.4
95-th percentile1.6
Maximum3.9
Range3.9
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.50273265
Coefficient of variation (CV)1.240834
Kurtosis19.472279
Mean0.40515706
Median Absolute Deviation (MAD)0.1
Skewness4.0787907
Sum49180.8
Variance0.25274012
MonotonicityNot monotonic
2025-02-06T13:42:17.155207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0.2 51554
42.5%
0.3 23699
19.5%
0.1 12158
 
10.0%
0.4 10062
 
8.3%
0.7 5506
 
4.5%
0.8 5450
 
4.5%
0.6 3716
 
3.1%
0.5 2352
 
1.9%
1.7 1275
 
1.1%
1.8 1273
 
1.0%
Other values (30) 4342
 
3.6%
ValueCountFrequency (%)
0 7
 
< 0.1%
0.1 12158
 
10.0%
0.2 51554
42.5%
0.3 23699
19.5%
0.4 10062
 
8.3%
0.5 2352
 
1.9%
0.6 3716
 
3.1%
0.7 5506
 
4.5%
0.8 5450
 
4.5%
0.9 219
 
0.2%
ValueCountFrequency (%)
3.9 24
 
< 0.1%
3.8 328
0.3%
3.7 319
0.3%
3.6 256
0.2%
3.5 86
 
0.1%
3.4 50
 
< 0.1%
3.3 35
 
< 0.1%
3.2 2
 
< 0.1%
3.1 6
 
< 0.1%
3 2
 
< 0.1%

FOG Index
Real number (ℝ)

High correlation 

Distinct1790
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0744193
Minimum0
Maximum120.4
Zeros30
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size948.5 KiB
2025-02-06T13:42:17.196841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.2
Q15.66
median8.67
Q311.67
95-th percentile18
Maximum120.4
Range120.4
Interquartile range (IQR)6.01

Descriptive statistics

Standard deviation5.5720706
Coefficient of variation (CV)0.61404156
Kurtosis8.8098758
Mean9.0744193
Median Absolute Deviation (MAD)3
Skewness1.5650952
Sum1101516.5
Variance31.047971
MonotonicityNot monotonic
2025-02-06T13:42:17.238739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.04 3494
 
2.9%
2 3451
 
2.8%
10 3445
 
2.8%
1.6 3372
 
2.8%
2.4 2803
 
2.3%
1.2 2739
 
2.3%
0.8 2590
 
2.1%
11.6 2568
 
2.1%
9.07 2476
 
2.0%
8.2 2389
 
2.0%
Other values (1780) 92060
75.8%
ValueCountFrequency (%)
0 30
 
< 0.1%
0.4 1218
 
1.0%
0.8 2590
2.1%
1 13
 
< 0.1%
1.2 2739
2.3%
1.32 5
 
< 0.1%
1.4 203
 
0.2%
1.48 10
 
< 0.1%
1.6 3372
2.8%
1.72 23
 
< 0.1%
ValueCountFrequency (%)
120.4 1
 
< 0.1%
85.13 1
 
< 0.1%
80.4 9
< 0.1%
74.85 1
 
< 0.1%
61.6 1
 
< 0.1%
57.52 1
 
< 0.1%
54.66 1
 
< 0.1%
51.28 1
 
< 0.1%
44.66 1
 
< 0.1%
44.29 1
 
< 0.1%

Flesch Reading Ease
Real number (ℝ)

High correlation 

Distinct2259
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.182635
Minimum-3093.59
Maximum206.84
Zeros0
Zeros (%)0.0%
Negative3071
Negative (%)2.5%
Memory size948.5 KiB
2025-02-06T13:42:17.278203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-3093.59
5-th percentile21.56
Q154.18
median69.79
Q383.66
95-th percentile114.12
Maximum206.84
Range3300.43
Interquartile range (IQR)29.48

Descriptive statistics

Standard deviation33.02487
Coefficient of variation (CV)0.49156854
Kurtosis853.39225
Mean67.182635
Median Absolute Deviation (MAD)14.58
Skewness-12.798857
Sum8155098.5
Variance1090.642
MonotonicityNot monotonic
2025-02-06T13:42:17.373676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83.32 1462
 
1.2%
75.88 1394
 
1.1%
66.4 1279
 
1.1%
92.8 1277
 
1.1%
50.5 1262
 
1.0%
81.29 1234
 
1.0%
90.77 1217
 
1.0%
68.77 1203
 
1.0%
100.24 1202
 
1.0%
73.85 1194
 
1.0%
Other values (2249) 108663
89.5%
ValueCountFrequency (%)
-3093.59 1
 
< 0.1%
-1739.99 1
 
< 0.1%
-1147.79 1
 
< 0.1%
-978.59 4
< 0.1%
-893.99 1
 
< 0.1%
-809.39 2
< 0.1%
-724.79 3
< 0.1%
-640.19 2
< 0.1%
-555.59 4
< 0.1%
-470.99 3
< 0.1%
ValueCountFrequency (%)
206.84 30
 
< 0.1%
121.22 746
0.6%
120.21 1150
0.9%
119.7 3
 
< 0.1%
119.19 1063
0.9%
118.89 1
 
< 0.1%
118.68 55
 
< 0.1%
118.48 1
 
< 0.1%
118.18 1051
0.9%
117.67 39
 
< 0.1%

depth
Real number (ℝ)

High correlation 

Distinct101186
Distinct (%)83.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49705832
Minimum9.28 × 10-18
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size948.5 KiB
2025-02-06T13:42:17.414137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9.28 × 10-18
5-th percentile0.026645969
Q10.32915201
median0.51381283
Q30.67263229
95-th percentile0.85203868
Maximum1
Range1
Interquartile range (IQR)0.34348028

Descriptive statistics

Standard deviation0.23563444
Coefficient of variation (CV)0.47405792
Kurtosis-0.50783966
Mean0.49705832
Median Absolute Deviation (MAD)0.16873744
Skewness-0.24016716
Sum60336.418
Variance0.055523587
MonotonicityNot monotonic
2025-02-06T13:42:17.456458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2014
 
1.7%
1.2 × 10-17567
 
0.5%
2.02 × 10-17432
 
0.4%
0.245853661 404
 
0.3%
0.245894725 360
 
0.3%
1.19 × 10-17334
 
0.3%
0.239850563 184
 
0.2%
0.178786802 173
 
0.1%
9.28 × 10-18169
 
0.1%
0.195146973 161
 
0.1%
Other values (101176) 116589
96.0%
ValueCountFrequency (%)
9.28 × 10-18169
0.1%
9.64 × 10-181
 
< 0.1%
9.66 × 10-1846
 
< 0.1%
9.69 × 10-1817
 
< 0.1%
9.8 × 10-18120
0.1%
9.82 × 10-1836
 
< 0.1%
1.03 × 10-17149
0.1%
1.08 × 10-179
 
< 0.1%
1.09 × 10-172
 
< 0.1%
1.12 × 10-1753
 
< 0.1%
ValueCountFrequency (%)
1 2014
1.7%
0.98548975 1
 
< 0.1%
0.978472438 1
 
< 0.1%
0.976354583 1
 
< 0.1%
0.975911527 1
 
< 0.1%
0.975716215 1
 
< 0.1%
0.974601953 1
 
< 0.1%
0.973759302 1
 
< 0.1%
0.972450951 1
 
< 0.1%
0.972182366 1
 
< 0.1%

breadth
Real number (ℝ)

High correlation 

Distinct101765
Distinct (%)83.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.66967431
Minimum0.045493357
Maximum1.6496107
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size948.5 KiB
2025-02-06T13:42:17.496107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.045493357
5-th percentile0.21839501
Q10.45677639
median0.64806394
Q30.85804621
95-th percentile1.2105752
Maximum1.6496107
Range1.6041173
Interquartile range (IQR)0.40126982

Descriptive statistics

Standard deviation0.29997378
Coefficient of variation (CV)0.44793981
Kurtosis0.0028850438
Mean0.66967431
Median Absolute Deviation (MAD)0.19958818
Skewness0.40909187
Sum81289.755
Variance0.089984269
MonotonicityNot monotonic
2025-02-06T13:42:17.538725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.045493357 2014
 
1.7%
1.076657762 581
 
0.5%
1.066136648 469
 
0.4%
0.858442265 404
 
0.3%
0.872999175 360
 
0.3%
1.093095606 339
 
0.3%
1.26598647 207
 
0.2%
1.2310041 184
 
0.2%
1.321873164 173
 
0.1%
1.532818084 172
 
0.1%
Other values (101755) 116484
96.0%
ValueCountFrequency (%)
0.045493357 2014
1.7%
0.049614877 1
 
< 0.1%
0.055886761 1
 
< 0.1%
0.061306332 1
 
< 0.1%
0.0627203 1
 
< 0.1%
0.063906686 1
 
< 0.1%
0.06424804 1
 
< 0.1%
0.067131507 1
 
< 0.1%
0.067356517 1
 
< 0.1%
0.068989897 1
 
< 0.1%
ValueCountFrequency (%)
1.649610684 129
0.1%
1.649213281 1
 
< 0.1%
1.648314745 1
 
< 0.1%
1.648212635 2
 
< 0.1%
1.645404283 1
 
< 0.1%
1.645222248 1
 
< 0.1%
1.642825624 1
 
< 0.1%
1.642720033 2
 
< 0.1%
1.641016631 1
 
< 0.1%
1.637482087 1
 
< 0.1%

valence
Real number (ℝ)

High correlation 

Distinct3676
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2822403
Minimum1.015
Maximum4.989
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size948.5 KiB
2025-02-06T13:42:17.580403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.015
5-th percentile2.731
Q14.112
median4.508
Q34.727
95-th percentile4.885
Maximum4.989
Range3.974
Interquartile range (IQR)0.615

Descriptive statistics

Standard deviation0.67229
Coefficient of variation (CV)0.15699493
Kurtosis3.5839261
Mean4.2822403
Median Absolute Deviation (MAD)0.265
Skewness-1.9021409
Sum519808.3
Variance0.45197384
MonotonicityNot monotonic
2025-02-06T13:42:17.623617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.782 196
 
0.2%
4.74 190
 
0.2%
4.758 189
 
0.2%
4.771 189
 
0.2%
4.786 188
 
0.2%
4.75 188
 
0.2%
4.812 187
 
0.2%
4.628 186
 
0.2%
4.776 185
 
0.2%
4.753 182
 
0.1%
Other values (3666) 119507
98.5%
ValueCountFrequency (%)
1.015 2
< 0.1%
1.03 1
< 0.1%
1.034 1
< 0.1%
1.036 1
< 0.1%
1.039 1
< 0.1%
1.049 1
< 0.1%
1.052 1
< 0.1%
1.053 1
< 0.1%
1.06 1
< 0.1%
1.061 1
< 0.1%
ValueCountFrequency (%)
4.989 3
< 0.1%
4.988 5
< 0.1%
4.987 1
 
< 0.1%
4.986 1
 
< 0.1%
4.985 2
 
< 0.1%
4.984 1
 
< 0.1%
4.983 1
 
< 0.1%
4.982 3
< 0.1%
4.981 3
< 0.1%
4.98 2
 
< 0.1%

sentiment_score_discrete
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size948.5 KiB
5
76213 
4
31684 
3
8812 
2
 
3300
1
 
1378

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121387
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 76213
62.8%
4 31684
26.1%
3 8812
 
7.3%
2 3300
 
2.7%
1 1378
 
1.1%

Length

2025-02-06T13:42:17.662070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-06T13:42:17.685358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
5 76213
62.8%
4 31684
26.1%
3 8812
 
7.3%
2 3300
 
2.7%
1 1378
 
1.1%

Most occurring characters

ValueCountFrequency (%)
5 76213
62.8%
4 31684
26.1%
3 8812
 
7.3%
2 3300
 
2.7%
1 1378
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 121387
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 76213
62.8%
4 31684
26.1%
3 8812
 
7.3%
2 3300
 
2.7%
1 1378
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 121387
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 76213
62.8%
4 31684
26.1%
3 8812
 
7.3%
2 3300
 
2.7%
1 1378
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 121387
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 76213
62.8%
4 31684
26.1%
3 8812
 
7.3%
2 3300
 
2.7%
1 1378
 
1.1%

arousal
Real number (ℝ)

Distinct121329
Distinct (%)> 99.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.029310592
Minimum-0.58547016
Maximum0.61454505
Zeros0
Zeros (%)0.0%
Negative39739
Negative (%)32.7%
Memory size948.5 KiB
2025-02-06T13:42:17.718844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.58547016
5-th percentile-0.48059656
Q1-0.046052439
median0.080451352
Q30.13862692
95-th percentile0.35535659
Maximum0.61454505
Range1.2000152
Interquartile range (IQR)0.18467936

Descriptive statistics

Standard deviation0.21419953
Coefficient of variation (CV)7.3079222
Kurtosis1.4013172
Mean0.029310592
Median Absolute Deviation (MAD)0.074388503
Skewness-0.91226869
Sum3557.8956
Variance0.045881439
MonotonicityNot monotonic
2025-02-06T13:42:17.761674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.578376331 5
 
< 0.1%
0.148219408 4
 
< 0.1%
-0.580260381 3
 
< 0.1%
-0.577658097 3
 
< 0.1%
0.126367412 3
 
< 0.1%
0.148596488 2
 
< 0.1%
0.11888753 2
 
< 0.1%
-0.577999896 2
 
< 0.1%
0.153124454 2
 
< 0.1%
-0.537838739 2
 
< 0.1%
Other values (121319) 121358
> 99.9%
ValueCountFrequency (%)
-0.58547016 1
< 0.1%
-0.585426161 1
< 0.1%
-0.58532197 1
< 0.1%
-0.585295935 1
< 0.1%
-0.584794675 1
< 0.1%
-0.584300215 1
< 0.1%
-0.58413353 1
< 0.1%
-0.584031357 1
< 0.1%
-0.583921868 1
< 0.1%
-0.583864011 1
< 0.1%
ValueCountFrequency (%)
0.614545049 1
< 0.1%
0.612567026 1
< 0.1%
0.610007374 1
< 0.1%
0.609742338 1
< 0.1%
0.607369539 1
< 0.1%
0.607229081 1
< 0.1%
0.606565651 1
< 0.1%
0.606281555 1
< 0.1%
0.605757399 1
< 0.1%
0.60545536 1
< 0.1%

Interactions

2025-02-06T13:42:14.147410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:04.492840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:05.087155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:05.672116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:06.318113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:06.961624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:07.569254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:08.254142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:08.862612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:09.499767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:10.185171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:10.823707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:11.508751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:12.180005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:12.779773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:13.502125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:14.186418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:04.531780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:05.123067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:05.708660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:06.354234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:06.999914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:07.607210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:08.290686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:08.897406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:09.537658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:10.222498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:10.865074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:11.548393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:12.216157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:12.817314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:13.539517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:14.223894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:04.566522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:05.161673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:05.742551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:06.389633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:07.036117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:07.646417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:08.328609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:08.932088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:09.576006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:10.260077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:10.903711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:11.584961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:12.252643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:12.855418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:13.576839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:14.260407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:04.602277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:05.197786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-06T13:42:06.425353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:07.072968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:07.686180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:08.364925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:09.021128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:09.613514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:10.298458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:10.944463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:11.622494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:12.288796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:12.896593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:13.614452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:14.354997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:04.638043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:05.232442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:05.869442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:06.460820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:07.109366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:07.724904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:08.401926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:09.057286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:09.653996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:10.336163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:10.984359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:11.660556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:12.323973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:12.936212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:13.652556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:14.395630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:04.675701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:05.268910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:05.907096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:06.499684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:07.148094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:07.763629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:08.440586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:09.093569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:09.694418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:10.377427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:11.084945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:11.700077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:12.361234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:12.978630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:13.692455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:14.435070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:04.714658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:05.306330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:05.944758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:06.537799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:07.188684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:07.803242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:08.478986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:09.131616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:09.735374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:10.418190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:11.126928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:11.740335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:12.400145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:13.021585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:13.734576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:14.473894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-06T13:42:11.856326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-06T13:42:08.632589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-06T13:42:11.280694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:11.896256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:12.552520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:13.196464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:13.904324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:14.631115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:04.898172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:05.487882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:06.132238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:06.724492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:07.378448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:08.050595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:08.671081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:09.317159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:09.932345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:10.618831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:11.319287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:11.931893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:12.588545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:13.238136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:13.946015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:14.671000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:04.937695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:05.523951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:06.168462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:06.762273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:07.417663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:08.089687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:08.708159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:09.352694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:09.971819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:10.661000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:11.357321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:11.969994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:12.625694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:13.342875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:13.985630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:14.707266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:04.973882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:05.558846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:06.203737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:06.797149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:07.454317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:08.130550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:08.744036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:09.387085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:10.062256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:10.699376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:11.393369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:12.008124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:12.662354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:13.382646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:14.022786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:14.746902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:05.012471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:05.596050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:06.240559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:06.884330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:07.492579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:08.173764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:08.783755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:09.423327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:10.104022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:10.741090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:11.431892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:12.047717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:12.702590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:13.423405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:14.064449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:14.785174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:05.051680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:05.636837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:06.281976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:06.923202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:07.532358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:08.214902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:08.824956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:09.463056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:10.147078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:10.782454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:11.472235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:12.088392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:12.742768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:13.463982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:42:14.107250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-06T13:42:17.801187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Deviation of star ratingsFOG IndexFlesch Reading EaseRatingarousalavg_ratingbreadthdepthhelpfulnessnum_of_enrollednum_of_ratingsnum_of_reviewsnum_of_top_instructor_coursesnum_of_top_instructor_leanerssentiment_score_discretetext_lengthtime_lapsedvalence
Deviation of star ratings1.0000.033-0.0110.942-0.145-0.795-0.1370.0760.180-0.473-0.535-0.512-0.313-0.4090.3820.030-0.028-0.388
FOG Index0.0331.000-0.7630.014-0.059-0.013-0.2220.1990.066-0.021-0.024-0.030-0.042-0.0560.0400.2950.006-0.020
Flesch Reading Ease-0.011-0.7631.0000.0000.0210.0150.146-0.175-0.0380.0460.0440.0500.0250.0510.035-0.0900.025-0.036
Rating0.9420.0140.0001.0000.1040.1330.1250.1010.0460.1300.1280.1350.0800.1070.3980.0730.0380.428
arousal-0.145-0.0590.0210.1041.0000.0940.133-0.097-0.0390.0850.0790.0790.0160.0540.164-0.0030.0570.281
avg_rating-0.795-0.0130.0150.1330.0941.0000.056-0.010-0.1310.5010.5870.5510.3840.4840.1020.0280.0100.196
breadth-0.137-0.2220.1460.1250.1330.0561.000-0.693-0.1240.0370.0380.0530.0680.0820.171-0.446-0.0360.267
depth0.0760.199-0.1750.101-0.097-0.010-0.6931.0000.1100.0230.0190.010-0.020-0.0190.1390.4210.035-0.168
helpfulness0.1800.066-0.0380.046-0.039-0.131-0.1240.1101.000-0.141-0.160-0.159-0.078-0.1090.0260.1660.012-0.129
num_of_enrolled-0.473-0.0210.0460.1300.0850.5010.0370.023-0.1411.0000.9390.9450.3840.6510.0970.1000.1140.178
num_of_ratings-0.535-0.0240.0440.1280.0790.5870.0380.019-0.1600.9391.0000.9830.4080.6180.0900.0920.2060.172
num_of_reviews-0.512-0.0300.0500.1350.0790.5510.0530.010-0.1590.9450.9831.0000.3760.5920.0980.0740.1850.174
num_of_top_instructor_courses-0.313-0.0420.0250.0800.0160.3840.068-0.020-0.0780.3840.4080.3761.0000.8590.072-0.065-0.3320.066
num_of_top_instructor_leaners-0.409-0.0560.0510.1070.0540.4840.082-0.019-0.1090.6510.6180.5920.8591.0000.085-0.039-0.1970.122
sentiment_score_discrete0.3820.0400.0350.3980.1640.1020.1710.1390.0260.0970.0900.0980.0720.0851.0000.0630.0410.780
text_length0.0300.295-0.0900.073-0.0030.028-0.4460.4210.1660.1000.0920.074-0.065-0.0390.0631.0000.128-0.079
time_lapsed-0.0280.0060.0250.0380.0570.010-0.0360.0350.0120.1140.2060.185-0.332-0.1970.0410.1281.0000.030
valence-0.388-0.020-0.0360.4280.2810.1960.267-0.168-0.1290.1780.1720.1740.0660.1220.780-0.0790.0301.000

Missing values

2025-02-06T13:42:14.851215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-06T13:42:14.969062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-06T13:42:15.203540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

course_nameRatingavg_ratingnum_of_ratingshelpfulnessReview_Textnum_of_reviewsnum_of_enrollednum_of_top_instructor_coursesnum_of_top_instructor_leanerstext_lengthtime_lapsedDeviation of star ratingsFOG IndexFlesch Reading Easedepthbreadthvalencesentiment_score_discretearousal
0foundations-of-cybersecurity54.82790554The course is well paced and they get you comfortable with the topics even though we do not have any sort of prior exposure in this field. It is very good for the beginners who are new to this field551098822332511153139404900.211.0085.020.6157550.6998354.16640.009651
1foundations-of-cybersecurity54.82790544Information was well organized, easy to learn, and study. with frequent note writing, and some breaks . You can learn a good brief summary of what's to come, and what to research more in the future.551098822332511153139365590.28.1184.980.5220350.7198173.8804-0.059593
2foundations-of-cybersecurity54.82790541For a foundation course, this one was easy to understand, it explained all basic concepts in a fluid way and built up the base for the upcoming courses. I'm eager to move on to the other courses now.551098822332511153139385650.29.7169.110.3857610.4812654.39550.254931
3foundations-of-cybersecurity54.82790532I think this is a great start for anyone who is starting from absolute zero. I think that since I've been toying with the idea of getting into Cybersecurity for 2 years now, it was a great refresher!551098822332511153139383260.210.7669.110.4129130.5039664.60350.111229
4foundations-of-cybersecurity54.82790524Surprised by the quality of this course. repeating items so you learn by seeing definitions and concepts over and over again while using great analogy to make difficult concept understandable.551098822332511153139305610.214.0056.250.6759410.5612664.57550.543238
5foundations-of-cybersecurity54.82790521! Cybersecurity is a critical and multifaceted field that involves protecting computer systems, networks, and data from various digital threats. Here is a review of the foundations of cybersecurity551098822332511153139293990.217.0314.970.7923270.5056073.7884-0.526757
6foundations-of-cybersecurity14.82790518In the certificate , why am i not getting my name printed ? Instead "Coursera Learner"?551098822332511153139165323.85.6672.830.5327690.4742212.04210.122483
7foundations-of-cybersecurity54.8279059Dear Instructors of the Foundations of Cybersecurity Course at Google,I wanted to take a moment to express my deepest gratitude for the incredible learning experience you provided throughout the course. Your expertise, dedication, and passion for cybersecurity have truly made a lasting impact on my journey in this field.Firstly, allow me to introduce myself. My name is Your Name, and I embarked on this course with a strong desire to deepen my understanding of cybersecurity and acquire the necessary skills to contribute meaningfully to the industry. As a lifelong learner, I am always seeking opportunities to expand my knowledge and stay updated with the latest developments in technology. This course seemed like the perfect fit to enhance my cybersecurity expertise.I chose to take this course because I firmly believe that cybersecurity is a critical aspect of our increasingly digital world. With the growing threats and vulnerabilities that organizations and individuals face, I wanted to equip myself with the knowledge and skills to make a tangible difference in securing digital systems and protecting sensitive information. The Foundations of Cybersecurity course seemed like the ideal starting point to build a strong foundation in this field.I am pleased to share that the course has exceeded my expectations in every way. From the very beginning, the course structure, content, and delivery were impeccable. The way you organized the modules and topics ensured a smooth learning journey, allowing me to grasp the fundamental concepts before diving into more advanced areas.What I truly loved about the course was the emphasis on practicality. The hands-on labs and simulations were invaluable in reinforcing the theoretical knowledge and providing a real-world perspective. Being able to apply the concepts in a practical setting not only enhanced my technical skills but also instilled confidence in my ability to tackle real-world cybersecurity challenges.Furthermore, the breadth of topics covered in the course was remarkable. From threat analysis to network security, encryption, incident response, and compliance, every aspect was explored in depth, providing a comprehensive understanding of the cybersecurity landscape. I appreciated the balance between theory and practical applications, as it allowed me to develop a holistic understanding of the subject matter.Your dedication as instructors was evident throughout the course. The quality of the course materials, including the informative videos, interactive quizzes, and additional readings, showcased the meticulous effort put into curating the content. Your ability to simplify complex concepts and communicate them effectively is commendable.The course has had a significant impact on my professional growth. Not only have I gained a solid understanding of cybersecurity fundamentals, but I have also acquired practical skills that I can immediately apply in real-world scenarios. The course has expanded my career prospects and opened doors to exciting opportunities in the cybersecurity field.I am truly grateful for the knowledge and insights I gained from this course. Your guidance and expertise have played a crucial role in shaping my cybersecurity journey. The impact you have made on my professional development is immeasurable.Thank you once again for your dedication, passion, and commitment to providing an exceptional learning experience. I am proud to have been a student in the Foundations of Cybersecurity course at Google, and I look forward to continuing my cybersecurity journey with the skills and knowledge I have acquired.With utmost appreciation,Jalal Saleem5510988223325111531395445390.213.6427.720.8864090.2535214.55650.095011
8foundations-of-cybersecurity34.8279059The questions on the quizzes were often meaningless and the multiple choice answers were unbelievably vague and ill-defined to the point where no answer was entirely correct.551098822332511153139275601.816.7344.070.8566040.1553221.80020.059838
9foundations-of-cybersecurity54.8279058The instructors for the course did an amazing job at presenting all of the information to us! The course is informative and definitely will expand your knowledge specifically over Cybersecurity.551098822332511153139305350.215.3339.330.6949000.5841004.64350.096451
course_nameRatingavg_ratingnum_of_ratingshelpfulnessReview_Textnum_of_reviewsnum_of_enrollednum_of_top_instructor_coursesnum_of_top_instructor_leanerstext_lengthtime_lapsedDeviation of star ratingsFOG IndexFlesch Reading Easedepthbreadthvalencesentiment_score_discretearousal
121377java-programming-arrays-lists-data24.731680Felt this was too vague for beginners, and too simple for those with experience. Spent a lot of time fighting with the quizzes to get it the answers it wanted, rather than focusing on writing good code.5151607101810714413716022.710.6478.086.984324e-010.6382052.2342-0.393498
121378java-programming-arrays-lists-data24.731680Please, focus on what you want the student to know from the course. You exaggerate a a lot explaining stuff.515160710181071441205322.78.0078.253.576219e-010.6201673.2063-0.349344
121379java-programming-arrays-lists-data14.731680Not really useful I would like to email in a PDF of my comments covering this course and the second course in this series5151607101810714412432083.711.2772.502.973635e-010.8316761.9532-0.154776
121380java-programming-arrays-lists-data14.731680A Very Hard course that you can't overcome it easily5151607101810714411015023.78.0086.713.030271e-010.5466742.3042-0.130942
121381java-programming-arrays-lists-data14.731680The course is very poorly designed for begineers515160710181071441815383.78.2071.826.560000e-170.9353361.84520.057882
121382introduction-to-applied-cryptography54.6660This course by Professor Keith Martin is a fast-paced introduction to the basics of cryptography and six important classes of applications.Even though the course is aimed at beginners, it contains a lot of material (e.g., on mobile telephony) that will be of interest to advanced students as well.What I appreciated most about the course was the broad perspective and the relaxed manner in which serious knowledge was taught.99693283454684520.413.2745.767.491413e-010.4442103.97140.037340
121383introduction-to-applied-cryptography54.6660Thank you so much during this 4 weeks, i gained more knowledges about cryptography in this course. This course really help me to learn in flexiblw time.99693283454275640.48.3683.156.469838e-010.7420234.8255-0.092934
121384introduction-to-applied-cryptography54.6660very useful and informative course.9969328345455640.410.0049.483.512218e-011.1153424.61550.099697
121385introduction-to-applied-cryptography54.6660Challenging and very enratainent.9969328345444540.411.6050.507.463779e-010.3147784.4905-0.346199
121386introduction-to-applied-cryptography54.6660very good education.9969328345433890.414.5334.591.772351e-021.0589444.51450.145036